# coding: utf-8

# In[19]:

# 0.78468

# In[20]:

import numpy as np

import pandas as pd

import warnings

warnings.filterwarnings('ignore')

from sklearn import preprocessing

# In[21]:

train_path = r'C:\Users\cbattle\Desktop\train.csv' # r'/home/adminn/桌面/train.csv'

test_path = r'C:\Users\cbattle\Desktop\test.csv' # r'/home/adminn/桌面/test.csv'

out_path = r'C:\Users\cbattle\Desktop\out.csv' # r'/home/adminn/桌面/out.csv'

train = pd.read_csv(train_path)

test = pd.read_csv(test_path)

print('train:',train.shape)

print('test:',test.shape)

# train.info()

# test.info()

# print(train.head())

# 属性列

# print([col for col in train])

# print([col for col in test])

# 策略

# ['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked']

#     drop          onehot   drop    0/1    num    num       num      drop     num      0/1    用S补空，onehot

# In[22]:

X = train.drop(['Survived','PassengerId','Name'], axis=1)

y = train['Survived']

Xtest = test.drop(['PassengerId','Name'], axis=1)

# print('X:',X.shape)

# print('y:',y.shape)

# print('Xtest:',Xtest.shape)

# In[23]:

key = [col for col in X if X[col].dtype != 'object' # numberic ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']

       or col == 'Sex'

       or col == 'Embarked'

       or col == 'Cabin'

      ]

X = X[key]

Xtest = Xtest[key]

# print(key)

def showNullNum(a,b):

    print(a.isnull().sum())

    print()

    print(b.isnull().sum())

    print('------------------------------------')

showNullNum(X,Xtest)

# Xtest['Fare'][Xtest['Fare'].isnull()] = Xtest['Fare'].median() # replace nan with median

# X = X.dropna(axis=0) # drop X and y in the same row

#-------------------------------------------------------------------------------

# Pclass    Ticket class

# 1 = 1st, 2 = 2nd, 3 = 3rd  onehot

# for i in X['Pclass'].unique():

#     X['Pclass_'+str(i)] = (X['Pclass']==i).astype(int)

#     Xtest['Pclass_'+str(i)] = (Xtest['Pclass']==i).astype(int)

# X = X.drop(['Pclass'],axis=1)

# Xtest = Xtest.drop(['Pclass'],axis=1)

#-------------------------------------------------------------------------------

# Sex

X['Sex'] = X['Sex'].apply(lambda i:1 if i=='female' else 0)

Xtest['Sex'] = Xtest['Sex'].apply(lambda i:1 if i=='female' else 0)

#-------------------------------------------------------------------------------

# Embarked

# 1 label encoding

X['Embarked'][X['Embarked'].isnull()] = 'S'

X['Embarked'] = X['Embarked'].map({'S':0,'C':1,'Q':2}).astype(int)

Xtest['Embarked'] = Xtest['Embarked'].map({'S':0,'C':1,'Q':2}).astype(int)

# or use sklearn.preprocessing.LabelEncoder

# print(X.head())

# print(Xtest.head())

# X['Embarked'][X['Embarked'].isnull()] = 'S'

# from sklearn import preprocessing

# le = preprocessing.LabelEncoder()

# X['Embarked'] = le.fit_transform(X['Embarked'])

# Xtest['Embarked'] = le.transform(Xtest['Embarked'])

# print(X.head())

# print(Xtest.head())

# 2 onehot

# for i in X['Embarked'].unique():

#     print(i, 'sum:', sum(X['Embarked']==i))

# X['Embarked'][X['Embarked'].isnull()] = 'S' # most_frequent

# for i in X['Embarked'].unique():

#     X['Embarked_type_'+i] = (X['Embarked']==i).astype(int)

#     Xtest['Embarked_type_'+i] = (Xtest['Embarked']==i).astype(int)

# X = X.drop(['Embarked'],axis=1)

# Xtest = Xtest.drop(['Embarked'],axis=1)

# print(X.head(10))

#-------------------------------------------------------------------------------

# Cabin

# has a cabin or not

# print(X.head(5))

Xtest['Cabin'] = Xtest['Cabin'].apply(lambda i:1 if isinstance(i,str) else 0)

X['Cabin'] = X['Cabin'].apply(lambda i:1 if isinstance(i,str) else 0)

# print(X.head(5))

#-------------------------------------------------------------------------------

# age and fare

# use median to replace nan

from sklearn.preprocessing import Imputer

ip = Imputer(strategy='median')

X = ip.fit_transform(X)

Xtest = ip.transform(Xtest)

print(np.isnan(X).sum(),np.isnan(Xtest).sum())

# In[24]:

from xgboost import XGBClassifier

xgb = XGBClassifier()

xgb.fit(X,y)

ans = xgb.predict(Xtest)

# from sklearn.tree import DecisionTreeClassifier

# from sklearn.ensemble import ExtraTreesClassifier

# from sklearn.svm import LinearSVC

# In[25]:

out = pd.DataFrame({'PassengerId':test['PassengerId'],'Survived':ans})

out.to_csv(out_path,index = False)

print('ok')

# In[26]:

from sklearn import preprocessing

le = preprocessing.LabelEncoder()

le.fit(['a','b','c'])

ans = le.transform(['a','a','c'])

print(ans)